Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88967
Title: Tensor-based angle estimation approach for strictly noncircular sources with unknown mutual coupling in bistatic MIMO radar
Authors: Guo, Yuehao
Wang, Xianpeng
Wang, Wensi
Huang, Mengxing
Shen, Chong
Cao, Chunjie
Bi, Guoan
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Bistatic MIMO Radar
Angle Estimation
Issue Date: 2018
Source: Guo, Y., Wang, X., Wang, W., Huang, M., Shen, C., Cao, C., & Bi, G. (2018). Tensor-based angle estimation approach for strictly noncircular sources with unknown mutual coupling in bistatic MIMO radar. Sensors, 18(9), 2788-. doi:10.3390/s18092788
Series/Report no.: Sensors
Abstract: In the paper, the estimation of joint direction-of-departure (DOD) and direction-of-arrival (DOA) for strictly noncircular targets in multiple-input multiple-output (MIMO) radar with unknown mutual coupling is considered, and a tensor-based angle estimation method is proposed. In the proposed method, making use of the banded symmetric Toeplitz structure of the mutual coupling matrix, the influence of the unknown mutual coupling is removed in the tensor domain. Then, a special enhancement tensor is formulated to capture both the noncircularity and inherent multidimensional structure of strictly noncircular signals. After that, the higher-order singular value decomposition (HOSVD) technology is applied for estimating the tensor-based signal subspace. Finally, the direction-of-departure (DOD) and direction-of-arrival (DOA) estimation is obtained by utilizing the rotational invariance technique. Due to the use of both noncircularity and multidimensional structure of the detected signal, the algorithm in this paper has better angle estimation performance than other subspace-based algorithms. The experiment results verify that the method proposed has better angle estimation performance.
URI: https://hdl.handle.net/10356/88967
http://hdl.handle.net/10220/46016
ISSN: 1424-8220
DOI: http://dx.doi.org/10.3390/s18092788
Rights: © 2018 by The Author(s). Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
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